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Grounded Theory in Software Engineering Research: A Critical Review and Guidelines


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Grounded Theory (GT) has proved an extremely useful research approach in several fields including medical sociology, nursing, education and management theory. However, GT is a complex method based on an inductive paradigm that is fundamentally different from the traditional hypothetico-deductive research model. As there are at least three variants of GT, some ostensibly GT research suffers from method slurring, where researchers adopt an arbitrary subset of GT practices that are not recognizable as GT. In this paper, we describe the variants of GT and identify the core set of GT practices. We then analyze the use of grounded theory in software engineering. We carefully and systematically selected 98 articles that mention GT, of which 52 explicitly claim to use GT, with the other 46 using GT techniques only. Only 16 articles provide detailed accounts of their research procedures. We offer guidelines to improve the quality of both conducting and reporting GT studies. The latter is an important extension since current GT guidelines in software engineering do not cover the reporting process, despite good reporting being necessary for evaluating a study and informing subsequent research.
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Grounded Theory in Software Engineering Research:
A Critical Review and Guidelines
Klaas-Jan Stol
Lerothe Irish Software Research
Centre, University of Limerick
Paul Ralph
Department of Computer Science
University of Auckland
New Zealand
Brian Fitzgerald
Lerothe Irish Software Research
Centre, University of Limerick
Grounded Theory (GT) has proved an extremely useful research
approach in several fields including medical sociology, nursing,
education and management theory. However, GT is a complex
method based on an inductive paradigm that is fundamentally
different from the traditional hypothetico-deductive research
model. As there are at least three variants of GT, some ostensibly
GT research suffers from method slurring, where researchers
adopt an arbitrary subset of GT practices that are not recognizable
as GT. In this paper, we describe the variants of GT and identify
the core set of GT practices. We then analyze the use of grounded
theory in software engineering. We carefully and systematically
selected 98 articles that mention GT, of which 52 explicitly claim
to use GT, with the other 46 using GT techniques only. Only 16
articles provide detailed accounts of their research procedures. We
offer guidelines to improve the quality of both conducting and
reporting GT studies. The latter is an important extension since
current GT guidelines in software engineering do not cover the re-
porting process, despite good reporting being necessary for
evaluating a study and informing subsequent research.
CCS Concepts
General and reference Empirical studies
Grounded theory, software engineering, review, guidelines
“And make-it-up-as-you-go-along may be OK, but then you
have to say, ‘I’m making-it-up-as-I-go-along, guys.’
In: Dialogue: More on Muddling Methods[57]
There is growing awareness that Software Engineering (SE)
research must consider social, cultural and human aspects of
software construction [6, 26, 62]. Scholars have consequently
adopted diverse research methods from the social sciences.
Qualitative research methods are increasingly employed in SE
research as evidenced by journal special issues on their use in
2007 [23] and 2011 [26]. One method that is attracting particular
attention is grounded theory (GT) [2, 47]. A quick search in the
Scopus database indicates the number of grounded theory studies
in computer science has been growing for the last decade (Fig. 1).
Early examples of the use of GT in software engineering are by
Carver [13] and Coleman and O’Connor [18].
Grounded theory is a method originally described by Glaser and
Strauss in their seminal book The Discovery of Grounded Theory
[38]. The goal of GT is to generate theory rather than test or
validate existing theory. GT is suitable for investigating questions
such as what’s going on here? [2].
As a relatively young discipline, SE has yet to establish and
validate abundant formal theories. Given the unique and novel
aspects of the underlying technology in SE, theories from other
disciplines may not be easy to borrow and adapt for SE. Inductive
approaches such as GT are therefore useful to construct a relevant
conceptual and theoretical foundation for the field.
Since its inception, GT has provided an extremely useful method-
ological approach in numerous disciplinesnotably medical
sociology [15], nursing [4], education [58] and management [52].
However, researchers have been criticized for using GT too
casually, without clarifying that they have appreciated the
intricacies of grounded theory, which is not only quite complex
but also based on an inductive paradigm that is entirely different
from the traditional hypothetico-deductive model [72]:
“‘Grounded theory’ is often used as rhetorical sleight of
hand by authors who are unfamiliar with qualitative
research and who wish to avoid close description or
illumination of their methods. More disturbing, perhaps,
is that it becomes apparent, when one pushes them to
describe their methods, that many authors hold some
serious misconceptions about grounded theory.”
It is therefore crucial that researchers appropriately design and
accurately report studies using inductive methods including
grounded theory. However, some software engineering articles
that claim to use GT manifest considerable discrepancies between
their description of what they have actually done and seminal GT
Figure 1. Rise of grounded theory studies in computer science
Source: Scopus (Aug 2015); search string: TITLE-ABS-KEY
(“grounded theory”), limited to “computer science”
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guidance. To assess the scale of this problem in the software engi-
neering literature, we posed the following research question:
Research Question: What is the state of practice of grounded
theory research in software engineering?
Several SE researchers have recently reported their experiences
using GT and these provide useful guidance for prospective GT
researchers [1, 17, 44]. However, this guidance does not extend to
reporting GT studies. Reporting is important because this pro-
duces the persistent record that supports extension and contributes
to the field’s cumulative body of knowledge. Furthermore, this
paper presents a review of almost 100 articles through which we
identify a number of key issues with GT studies in SE.
We emphasize that our purpose is not to pedantically analyze and
criticize the papers included in our study, nor to criticize the au-
thors of those studies in any way. Instead, we draw attention to
prevalent misunderstandings of grounded theory as an approach,
and contend that only research that embodies GT’s core principles
(Sec. 2.1) should claim to be a grounded theory study. Based on
the results below, we explore numerous considerations for con-
ducting and reporting grounded theory and uncover challenges
peculiar to software contexts. Our contribution is consequently
fourfoldwe provide (1) an in-depth comparison of the three
main variants of GT; (2) a critical analysis of the state of practice
of the use of grounded theory in the software engineering litera-
ture; (3) a set of considerations for conducting and reporting GT
studies in SE; (4) three significant challenges for applying GT to
software engineering phenomena.
This paper proceeds as follows. Section 2 presents a brief history
of grounded theory (including its terminology and philosophical
foundations) and a comparison of the different GT versions.
Section 3 presents the research design that we employed. Section
4 presents the analysis and results of our study. Section 5 dis-
cusses the results and offers a checklist for future GT studies.
Section 6 concludes the paper.
2.1 Key Components of Grounded Theory
Grounded Theory refers to a method of inductively generating
theory from data [38]. GT studies often focus on unstructured text
(e.g. interview transcripts, documents, field notes); however, they
may also include structured text, diagrams and images, and even
quantitative data [35].
For the presentation of our analysis in Sec. 4, it is imperative to
establish the key components of grounded theory. While GT has
several variants (discussed in Sec. 2.3) they share many core fea-
tures, including the following:
Limit exposure to literature. Rather than beginning with a
comprehensive literature review, grounded theory proponents
(e.g. [19, 35]) recommend limiting exposure to existing
literature and theories to promote open-mindedness and pre-
empt confirmation bias (see Sec. 2.3 for different positions
regarding the literature). A major reason to limit study of the
literature is to prevent the researcher from testing existing
theories, or thinking in terms of established concepts.
Treat everything as data. When Glaser says, “all is data,”
he means allqualitative data, quantitative data, semi-struc-
tured data, pictures, diagrams, videos and even existing theo-
ries and literature [36, 69].
Immediate and continuous data analysis. The researcher
begins analyzing data immediately and does not finish
collecting data before beginning analysis—data collection
and analysis are simultaneous [16], and subsequent data
collection is driven by theoretical sampling, discussed next.
Theoretical sampling. The researcher identifies further data
sources based on gaps in the emerging theory or to further
explore unsaturated concepts. Theoretical sampling is
indeterministic, as opposed to conventional sampling
techniques [16] (see theoretical saturation below).
Theoretical sensitivity, which refers to the researcher’s
ability to conceptualize, and to establish relationships
between concepts, lies at the heart of developing grounded
theory. Both Glaser [35] (Ch. 5) and Strauss and Corbin [68]
highlight the role of creativity in this process.
Coding. The researcher uses inductive and abductive logic to
construct analytical codes and infer theoretical categories
from the data by labeling ‘incidents’ and their properties. The
researcher does not classify data into a preconceived coding
scheme, or infer categories from logically deduced
hypotheses [16]. Glaser and Strauss [38] did not use the term
abduction but emphasized induction to distance themselves
from the deductive theorizing that was prevalent at the time
of their publication. Both Glaser and Strauss later admitted a
role for deduction in GT [36, 70].
Memoing. The researcher writes memos (e.g. notes, dia-
grams, sketches) to elaborate categories as they emerge,
describe preliminary properties and relationships between
categories, and identify gaps [16]. These memos play such an
important role in theory generation that Glaser baldly stated
that, “if the researcher skips this stage, he is not doing
grounded theory[34] (Ch. 5, emphasis original).
Constant comparison. From the start of the study, the re-
searcher constantly compares data, memos, codes and cate-
gories [8]. Both categories and data interpretations evolve
and saturate until they ‘fit’ the data [34].
Memo sorting, also called theoretical sorting is the
continuous process of oscillating between the memos and the
emerging theory outline to find a suitable fit for all categories
that resulted from the coding [34, 70]. Like memoing, Glaser
argues that sorting cannot be skipped [35].
Cohesive theory. The researcher attempts to move beyond
superficial categories and develop a cohesive theory of the
studied phenomenon.
Theoretical saturation. The researcher stops collecting and
analyzing data when theoretical saturation is reached. Theo-
retical saturation refers to the point at which a theory’s com-
ponents are well supported and new data is no longer trig-
gering revisions or reinterpretations of the theory [34].
While this list of core features is by no means a complete descrip-
tion of grounded theory (both Glaser and Strauss have written
numerous books to explain GT [34-36, 71]), it does highlight
some distinctions from more traditional, deductive research meth-
ods. The above, however, largely ignores the differences between
the various versions of GT, discussed in Section 2.3.
2.2 Philosophical Foundations
Research approaches are commonly (and simplistically) classified
into two broad groups based on the epistemological positions of
positivism and interpretivism [40]. GT can be confusing because
it does not fit cleanly into either group. We briefly discuss
ontology and epistemology, and then focus on how GT resists the
classification of positivism and interpretivism.
The positivist approach has long been applied in the physical
sciences, and has led to tremendous growth of knowledge in the
area. It comprises five pillars [43]:
Unity of the scientific method: the same approach to
knowledge acquisition applies to all forms of enquiry.
Search for causal relationships: science aims to find
regularity and causal relationships among studied elements.
Belief in empiricism: sense-experience is the only source of
knowledge but subjective perception is not acceptable.
Science (and its process) is value-free: science has no
intrinsic values or perspectives; science is independent of
politics, ideology, morality, society and culture.
Science is founded upon logic and mathematics: causal
relationships are demonstrated quantitatively, using the
universal language of math and the formal basis of logic.
Positivism assumes that: (1) the universe behaves according to
inalterable, discoverable laws; (2) systems are merely the sum of
their components (reductionism); (3) science should be
reproducible, reliable, rigorous and objective. Different scientists
observing the same phenomenon should therefore reach
equivalent conclusions.
Interpretivism makes the opposite assumptions (cf. [41]): (1) no
universal truth or reality exists, rather, “the important reality is
what people imagine it to be” [9]; (2) systems exhibit emergent
behaviors not reducible to their component parts [33, 48]; (3)
social science, which aims to understand and to interpret human
behavior, is fundamentally different from natural science, and
natural science methods including quantitative measurement,
statistical significance and hypothesis testing are insufficient for
understanding social phenomena [76]. Therefore, formulating
hypotheses is not relevant to an interpretivist study.
Understanding and explaining the social world requires emotion
and empathy, which preclude pure objectivity [76]. Interpretivists
have attacked positivism for promoting the myth of objectivity
[56] and Berger and Kellner point out that direct access to facts
and laws ... is never possible, no matter what one’s standpoint ...
there is no magic trick by which one can bypass the act of inter-
pretation” [5]. Interpretivists prefer qualitative methods,
including interviews, case studies, ethnography and action
research, arguing that these keep the researcher grounded in the
first-order, primary, lived concepts of everyday life” [21].
While positivism and interpretivism can be cast as polar opposites
[31], many studies do not sit neatly in either paradigm. We have
experiments where the dependent variable is ‘measured’ by com-
bining the subjective ratings of expert judges [55], case studies
with upfront hypotheses [63], interview studies where text is ana-
lyzed quantitatively [61] and mixed-method inquiries that com-
bine questionnaires with case studies [60]. All qualitative data
can be coded quantitatively” by counting words and categorizing
statements; meanwhile all quantitative data is based on
qualitative judgment” because we have to make assumptions to
interpret the numbers [73]. More fundamentally, these groups
involve several interconnected philosophical positions that cannot
be reduced to a single spectrum, let alone a Boolean variable.
It is easy to mistake GT as a qualitative or interpretivist method
because many GT studies focus on collecting and analyzing
unstructured text. However, GT was developed in the 1960s,
during the ontological and epistemological shift from positivism
and objectivism to social constructionism and postmodernism. GT
stems from a dissatisfaction with the way research was done,
whereby new researchers were trained as theoretical serfswho
tested the theories oftheoretical capitalists[34] (p. 9), which
could lack relevance to the real world. GT was developed due to a
desire to build theories more rigorously and dispassionately by
grounding them in objective reality.
2.3 Versions of Grounded Theory
Although Glaser and Strauss never explicated their epistemologi-
cal position in Discovery (and Glaser later argued that GT is
paradigm-agnostic), their terminology reflects an objectivist
stance. The title of their seminal book is The Discovery of
Grounded Theory[38] rather than for example Sensemaking with
grounded theorythe term discovery suggests that an objective
reality exists out there waiting to be discovered. Glaser speaks of
an indicator-concept model, analysis of a core variable, and aims
for parsimony in the developed theory, reflecting a position that
aligns with objectivism. Both Glaser and Strauss and Corbin also
used objectivist terminology in their definitions of theory as a set
of concepts and relationships among them that together offer
explanations and predictions (i.e. causality) [34, 68]. While Glaser
maintains that GT is independent from any philosophical stance,
Corbin has gradually shifted towards interpretivism [19].
Meanwhile, Charmaz (a student of Glaser), developed
constructivistgrounded theory by reinterpreting GT from a con-
structivist’s stance [16] that is closely connected to interpretivism.
Due to extensive discussions on what constitutes grounded theory,
it has been labeled a ‘contested concept’ [11]. Since Glaser and
Strauss’s seminal book, GT has seen considerable evolution
resulting in the emergence of different versions. Denzin lists no
less than seven different versions [22], although he does not
specify the differences between all of them. It is now widely
acknowledged that there are at least three main streams of GT [1]:
Glaser’s GT (classic or Glaserian GT); Strauss and Corbin’s GT
(Straussian GT); and Charmaz’s constructivist GT.
Glaser’s perspective is well reflected in the fact that he refers to
his version of grounded theory as classic” GT. He strongly disa-
grees with Strauss and Corbin’s version of GT [35] and has ar-
gued that Strauss and Corbin’s method is not grounded theory, but
refers to it as “full conceptual description” [35]. Furthermore,
Glaser has called constructivist grounded theory a “misnomer”
[37]. In this paper, we accept any version of grounded theory as
‘grounded theory’although we will argue below that
consistency with a particular version is important. Table 1
summarizes some of the key differences between the three main
strands of GT. An additional difficulty in comparing GT versions
is that Straussian GT is still evolving, as briefly mentioned above.
Of the three main versions of GT, the difference between classic
and Straussian GT has been discussed most extensively [10, 42,
51]. Classic GT can be characterized as having a strong focus on
emergence (of research questions, of codes, of theory), whereas
Straussian GT meticulously suggests a set of ‘mini-steps.’ This
difference in focus on emergence is captured succinctly by Stern:
“Strauss, as he examines the data, stops at each word to ask,
What if?’ Glaser keeps his attention focused on the data and
asks, ‘what do we have here?’” [64] (our emphasis). Glaser
requires any concept to be grounded in the data, whereas Strauss
and Corbin go beyond the data by asking various questions on
what might be to develop the emerging theory [35] (Ch. 8).
Strauss’s approach has been described as “more free-wheeling
flights of imagination,” which contrasts strongly with Glaser’s
faithfulness to the data.
There is little agreement on what constitutes theory. In classic GT,
theory consists of concepts that are related to one another,
offering explanation and prediction. Constructivist GT
emphasizes understanding and acknowledges that data,
interpretations, and resulting theory depend on the researcher’s
view. In practice, however, such ontological and epistemological
differences are rarely apparent in generated theories.
Table 1. Some of the key differences between the three main strands of grounded theory
Classic / Glaserian grounded theory
Constructivist grounded theory
Should not be defined a priori, but
emerge from the researchthis makes
the RQ relevant to the field. The re-
searcher starts with an ‘area of interest.’
Literature in other areas may be
consulted to increase the researcher’s
“theoretical sensitivity.” Defining a RQ
a priori is considered ‘forcing’ [35].
Research begins with “initial
research questions,” which evolve
throughout the study [16].
Role of the
An extensive literature review should be
delayed until after the theory is
emerging to prevent the influence of
existing concepts on the emerging
theory. Until the researcher has defined
the RQ, it is not clear which literature
should be consulted. Existing concepts
such as gender and age should not be
included a priori, but must ‘earn’ their
way into the emerging theory.
Acknowledges not only Glaser’s
reasons for delaying the literature
review but also the impracticality of
this strategy. Charmaz highlights
the need to tailor a literature review
to fit the purpose of the GT study
[16] (p. 306).
Open coding: ‘fracturing’ of the data;
line by line coding is recommended to
achieve full theoretical coverage, but
does not reject coding sentences or
paragraphs, or whole documents [35].
Selective coding: delimiting coding to
only those variables that relate to one
(or in some cases, several) core
variables to establish a parsimonious
theory. The core variable guides further
data collection.
Theoretical coding: establishing
conceptual relations between
substantive codes, resulting in the
development of hypotheses. Glaser
proposes several ‘coding families,’
which are theoretical codes that can be
used by researchers, though these must
‘earn’ their way into the emerging
theory (e.g. the Six C family in Fig. 4).
Initial coding: examining data
word-by-word, line-by-line or
incident-by-incident to make sense
of the text without injecting the
researcher’s assumptions, biases,
motivations. Similar to Glaser’s
open coding. Charmaz recommends
“coding with gerunds.”
Focused coding: selecting
categories from the most frequent
or important codes, and using them
to categorize the data; does not
require a single core category or
Theoretical coding: specifying the
relationship between categories to
integrate them into a cohesive
What is this data a study of? [34]
What category or what property of
what category does this incident in-
What is actually happening in the
What is this data a study of? [16]
What do the data suggest? Pro-
nounce? Leave unsaid?
From whose point of view?
What theoretical category does
this specific datum indicate? [16]
Objectivism: There exists a single,
correct description of reality; the
researcher therefore discovers grounded
theory from data [11].
Social constructionism: social
reality is constructed by our
individual and collective action. GT
emerges from “shared experiences
and relationships with participants”;
Observers are not neutral [16].
The generated categories must fit the
data, the theory should work (it must be
able to explain or predict what will
happen); the theory must have
relevance to the action of the area, and
the theory must be modifiable as new
data appear [34] (p. 4-5).
Credibility (e.g. is there sufficient
data to merit claims?), originality
(do your categories offer new
insights?), resonance (does the GT
make sense to participants),
usefulness (does the GT offer use-
ful interpretations?) [16] (p. 337).
While the 1998 edition of Strauss and Corbin’s book specifies
open, axial and selective coding, the 2008 edition (authored by
Corbin alone after Strauss’ death in 1996, making the term
Straussian GT a misnomer and Corbinian more appropriate) no
longer defines open and axial coding as separate activities [74].
This paper focuses on the 1998 version since it is very prevalent
(in particular axial coding). As Table 1 shows, the three variants
differ in their position with respect to key elements such as the
role of the literature, but also in terminology and order of
practices (e.g. coding procedures). For example, Strauss and
Corbin interpret selective coding differently from Glaser.
Furthermore, Strauss increasingly saw GT as a verificational
method [16], a position that Glaser strongly rejects [35].
3.1 Study Identification and Selection
To investigate the state of practice of GT research in SE, we
reviewed a selection of articles reporting GT studies. We adopted
an automated search strategy; that is, we collected our sample by
searching specific online databases using specific search strings
(see below). We chose this over manually browsing selected
publication outlets because it is more efficient and replicable. We
pilot tested several search strings. For example, we conducted a
search on grounded theory,but this resulted in thousands of
papers from other disciplines. We also tried limiting the search to
the title, abstract and keywords, but some GT studies appear not
to use the term ‘grounded theory’ in any of these fields. Based on
this pilot test, we adopted the following query.
Search String: grounded theoryAND software engineering
We searched Scopus, IEEE Xplore, the ACM Digital Library and
ScienceDirect. We excluded Wiley Online and SpringerLink, as
these are subsumed by Scopus. We adapted the search string to
the specific characteristics of each database. Further constraints
were introduced case-by-case to eliminate obviously irrelevant
papers. Combining the search results and removing duplicates
produced an initial dataset of 1,763 papers (Table 2). As this
dataset is too large for manual analysis, we focused on articles
published in well-known, peer-reviewed SE journals (Table 3).
We did not consider conference contributions because journal
papers tend to have endured greater review, be more polished and
have more liberal page limits. We also did not consider articles
from peer-reviewed magazines including Communications of the
ACM and IEEE Software because they tend to have briefer
methodological descriptions, given their practitioner-oriented
focus. In the interests of representativeness, we further excluded
specialist journals such as Requirements Engineering and the
International Journal of Open Source Software and Processes.
The selected journals coincide with those used in previous
reviews (e.g. [39], except magazines as stated). We further added
the Software Quality Journal and the journals that descended from
the Journal on Software Maintenance: Research and Practice.
Table 2. Searched databases and search constraints
Search constraints
N/A (full text)
Computer Science only (full text)
IEEE Xplore
Search on metadata only
Title, Abstract, Keywords only
Table 3. Selected journals and number of papers included
Information and Software Technology
Journal of Systems and Software
IEEE Transactions on Software Engineering
Empirical Software Engineering
Software Process: Improvement and Practice a
Journal of Software: Evolution and Process b
Software Quality Journal
ACM Trans. Software Engineering and Methodology
Journal of Software Maintenance and Evolution:
Research and Practice c
a Merged with J Software: Evolution and Process in 2012
b Successor of J Softw Maint Evol Research & Practice since vol. 24, 2012
c Vol. 1-12 published as J Software Maintenance: Research and Practice
We removed editorials, secondary studies (systematic reviews),
and articles that present methodological reflections on the use of
GT, rather than a specific GT study (e.g. [1, 12, 18, 46, 59]),
resulting in a final set of 98 papers (available in an appendix
[67]). Fig. 2 shows the articles’ distribution of publication year.
3.2 Data Extraction
We read all 98 papers to investigate the following questions.
What is claimed concerning the use of grounded theory? (e.g.
“we used grounded theory,” “we took a grounded theory
approach,“the data were coding using GT techniques”);
To what extent are different versions of grounded theory
discussed and used? To what extent do papers state their
epistemological stance?
Is grounded theory mentioned in the title, keywords, abstract,
or research question (or objective / topic / purpose)?
What specific GT techniques and practices are used? (e.g.
open coding, constant comparison, memoing);
How is data collected and analyzed?
What do GT studies produce and how do they present it?
(e.g. as a diagram);
Was the literature review (if any) conducted before, during or
after the study; was the resulting theory (if any) integrated
back into the literature?
All information was recorded in a spreadsheet. We also took
extensive notes concerning interesting findings that did not fit in
our predefined questions. In several studies, for example, we
noted clear deviations from GT principles, such as the use of
(preconceived) ‘seed categories’ to guide initial analysis which is
viewed as inappropriate in GT. The data extraction and coding
was done by the primary author, which was reviewed by the
remaining authors.
Figure 2. Distribution of publication year of selected articles
Note: Search conducted in Spring 2015, hence the drop in 2015.
In this section we address the use of GT, the level of detail
presented, variants of GT and the type of output of studies.
4.1 Grounded Theory “Use” is Ambiguous
We analyzed all 98 articles to investigate their claim of using
grounded theory, and found that many claims are quite ambigu-
ous. Fig. 3 (Box 1) shows that almost half (n=46) of the surveyed
articles (n=98) merely borrow from grounded theory; for example:
“Using concepts of grounded theory […]”
data analysis was carried out using a modified version
of Grounded Theory
Fifteen articles (Box 1.1 in Fig. 3) state that they use an approach
that resembles, adapts, or is inspired by grounded theory, but do
not in actual fact present a grounded theory study. An example of
such a claim is: In a method similar to the first step in grounded
theory (Glaser and Strauss 1967) […] we identified a set of cate-
gories. Such studies are clearly not grounded theory studies.
Eighteen articles (Box 1.2 in Fig. 3) do not use the term ‘grounded
theory’ in the main text at all (but only in its bibliography).
Rather, they mention specific techniques such as ‘coding’ or
‘theoretical saturation’ and cite seminal works on grounded
theory, such as Glaser and Strauss’s Discovery book [38].
Thirteen other articles (Box 1.3) state that they use grounded
theory ‘techniques’ or ‘procedures,’ and in most cases refer to
coding and constant comparison. One example of such a statement
is: The ‘Open Coding’ and ‘Theoretical Coding’ techniques of
Glaser (1978) have been applied iteratively to identify different
categories and their properties.”!Such statements do not claim
that GT was used, merely GT techniques. In several cases, authors
explicitly acknowledge that their study is not a GT study.
This borrowing rhetoric is unusual in research methodology. We
do not recall ever reading about studies that “use randomized
controlled trial techniques,” were “inspired by survey methodol-
ogy,” or “adopted a modified questionnaire approach.” Claiming
to “use grounded theory techniques” rather than GT wholly sug-
gests that authors are aware that GT is a comprehensive research
method from which they are borrowing certain elements.
The remaining 52 papers (Box 2 in Fig. 3) explicitly claim to use
GT. Typical examples of such claims include (e.g. [30]):
“Using a grounded theory approach […]”
We used grounded theory to […]”
“We generated a grounded theory”
Figure 3. Breakdown of the articles included in our review
However, deciding whether or not a study uses grounded theory is
far from trivial. While some articles clearly claim to use grounded
theory, the phrasing of these claims varies substantially and some
are ambiguous. For example, some studies use a “grounded theory
approach.” In the absence of further clarification, we assume this
means GT was used, however, it could be interpreted as an
approach based on GT. This made it more difficult to decide
whether or not the authors were actually claiming to use grounded
theory. This is simultaneously a potential threat to the validity of
our findings and a surprising finding itself. While our exact count
(52 studies making claims to use GT) should be interpreted with
caution, the fact that this is ambiguous, and any large proportion
of studies borrowing from a method rather than using it, is
unusual and potentially problematic for a sound evaluation of
such a study.
Of the 52 studies making a claim to have used GT, four studies
(Box 2.1) deviated so sharply from GT that they have not used
grounded theory at all. In three cases, the authors developed a set
of preliminary categories, which were then combined with a
“grounded theory approach”starting with a classification from
the literature is highly suspect, even when considering Strauss and
Corbin’s quite liberal use of the literature (see Table 1).
Of the 98 articles included in our review, six used the term
‘grounded theory’ in the title and 14 specified ‘grounded theory’
as a keyword. This suggests that grounded theory was essential to
these studies rather than an afterthought. While clearly no
conclusion should be drawn based on the presence of GT as a
keyword, given the limitations of some journals on the number of
keywords (as low as three), it might suggest that these authors
more consciously wished to signal the role of GT in their study.
4.2 Many Studies Present Little to No Detail
Of the 52 articles claiming to have done a grounded theory study,
18 (Box 2.1 in Fig. 3) present no details at all beyond claims such
as: [we] used a grounded theory approach for data gathering
and data analysis.” In some cases, a brief and usually incomplete
summary of grounded theory is provided, for example, by stating
that grounded theory consists of three coding phases. Besides
being incomplete, it also suggests coding happens in three distinct
phases, which is not what Glaser or Strauss had in mind. Many of
these articles state that the conceptualization presented in those
articles were developed using grounded theory, without shedding
any light on the process through which this was done.
We further inspected the 30 articles (Box 2.3) that present signifi-
cant methodological details, to investigate the extent to which
different GT practices are mentioned and used (Table 4). While
GT is not reducible to a set of independent practices, one still
expects GT studies to report details on key practices associated
with GT (cf. Sec. 2.1).
However, many authors use GT techniques à la carte. Fewer than
half of the 30 articles describe or confirm the use of key practices,
such as simultaneous data collection and analysis (n=13), mem-
oing (n=12), memo sorting (n=4), constant comparison (n=13), or
theoretical sampling (n=12). Fifteen articles confirm that data
collection continued until theoretical saturation was reached. All
but one article discuss data sources, elucidate data collection and
describe coding practices. Details varied from a brief paragraph to
an extensive presentation. We also found misinterpretations of
key practices. One article claimed theoretical sampling, but
instead of collecting additional data to further investigate as of yet
unsaturated concepts in the emerging theory, a number of case
companies were selected seemingly a priori based on their
experience in the area that the researchers were investigating.
[2] Explicitly
claiming GT
[1] Using GT
[2.3.3] Coding details
[2.3] Detailed
[2.2] Deviating from GT
[2.3.1] Comprehensive
and detailed
[2.1] No details at all
[2.3.2] Comprehensive
[1.2] “GT” not mentioned, only specific
[1.1] Adapted, inspired, resembles GT
[1.3] Claiming GT techniques
Table 4. Grounded theory practices used GT (n=52)
Papers reporting
GT Practice details reported
Simultaneous data collection and analysis
Data sources and collection
Theoretical sampling
Memo sorting
Constant comparison
Theoretical saturation
Sixteen articles (Boxes 2.3.1, 2.3.2) provide a comprehensive
presentation of their research method, of which five articles
present extensive documentation about the GT research process
[2, 18, 45, 47, 49]. Fourteen other articles provided details on the
coding process only (Box 2.3.3).
4.3 Many Studies Ignore GT Variants
As discussed in Sec. 2.3, GT has several variants with significant
differences with regards to the use of the literature, specific
coding practices, and reflections on the role of the researcher in
the research process. Of the 52 articles that claim to use grounded
theory, 39 did not acknowledge the existence of different variants.
To investigate which sources authors might have consulted in
their study design, we looked at the citations to seminal GT
works. Of the 39 articles that do not claim a specific GT variant,
10 cited works on classic GT (Glaser, Glaser & Strauss), 13 cited
works on Straussian GT (Strauss, Strauss & Corbin), and none
cited constructivist GT. Thirteen articles cite conflicting seminal
works on GT (e.g. [16, 19, 35]) without acknowledging any
differences or indicating whose guidance they are following. Two
articles cite works on all three variants of GT. One interpretation
of this is that authors are now aware of the differences, and, in
seeking to confer legitimacy on their research, provide copious
references to several seminal works. However, we would argue
that, had the authors actually read all three works, the existence of
different variants would have been likely acknowledged.
In several cases we found inconsistent usage of the claimed
variant of grounded theory. Two articles claim or cite classic GT
but use axial coding, a Straussian practice (Sec. 2.3). Another
article claims to use Straussian GT, but uses one of the coding
families offered by Glaser for increasing theoretical sensitivity
[34] (p. 74).
Table 5. Grounded theory variants acknowledged (n=52)
Grounded theory variant claimed
Acknowledgment of different GT variants
Explicit claim classic GT
Explicit claim Straussian GT
Explicit claim constructivist GT
Variants not acknowledged
Citing classic (Glaser / Glaser & Strauss)
Citing Straussian (Strauss / Strauss & Corbin)
Citing constructivist (Charmaz)
Citing a combination of the above
Citing others
Epistemology acknowledged
Interpretivist or constructivist
Three articles do not refer to any of the seminal texts on GT but
refer to other sources. These may be innocent mistakes or
benevolent simplifications. Alternatively, and more worryingly,
they may indicate researchers who are presenting their research
under the guise of techniques they have heard of, but not
Thirteen papers, however, do acknowledge the distinction
between classic and Straussian GTsome in more detail than
others. For example, one article stated that it incorporated “a
Strauss and Corbin grounded theory approach to data gathering
and analysis, whereas other articles laid out the differences
between the variants in detail. Of these, five claim to use classic
GT, the other eight Straussian GT. None of the articles in our
sample explicitly claim to use Charmaz’s constructivist GT.
Finally, only five articles state an epistemological position; in all
cases the authors claim their study to be an interpretivist one. In
four of those cases, reference was made to seminal works by
Glaser, and Strauss and Corbin, which align more closely with
positivism, as outlined in Sec. 2.
4.4 FewGT” Studies Generate Theory
Since grounded theory is a method of generating theory, we
investigated the extent to which the 52 studies claiming to have
used GT developed theories. While it depends on one’s definition
of theory, few of the studies appear (or claim) to develop a theory,
even though “a lack of existing theoriesin a particular area is
often given as a motivation to conduct a GT study.
Eight articles presented contributions that were clearly cohesive
theories consisting of constructs and relationships, while a ninth
article presented a set of hypotheses that could be considered a
theory. Some of the topics theorized by these studies include:
How is the software development process managed?
How do software processes form and evolve?
How do self-organizing agile teams self-organize?
Some articles present ‘theory’ in alternative forms instead of a set
of concepts and relationships. For example, Hoda et al. [47]
presented six roles that members of agile teams assume. Together
these roles provide an explanation for the “social” process of self-
organization in agile teams, and as such they go beyond a mere
taxonomy of roles. Therefore, we argue such a coherent set of
findings can be considered a theory.
In most cases, articles presented a graphical representation of the
theory, usually simple boxes-and-arrows diagrams, to illustrate
theoretical concepts and relationships. Three articles use Glaser’s
‘Six C’ coding family [34] (p. 74) for visualization (e.g. Fig. 4).
Other articles synthesized their results into various other types of
contributions, including:
Conceptual frameworks, such as a framework of factors that
influence Software Process Improvement initiatives [29];
Conceptual models, such as a model of the process for
managing collaborations in open source [7];
A set of factors, such as success factors for globally-
distributed XP projects [53];
A set of themes or categories, such as a set of categories
representing the characteristics of product managers [54].
Such contributions are useful as they offer new foundations for
empirical studies, but often they do not form a ‘theory’ that, in
Glaser’s words, “account for a pattern of behavior.” We observe
that studies that produce a ‘set of themes’ rather than a theory tend
only to borrow discrete practices from GTwhat we call
grounded theory à la carte.
Figure 4. Example of the Six C coding family (from: [45])
Finally, ten articles present mere description. In many cases, the
study’s results are structured based on a set of research questions,
which are answered in detail using quotes from participants. This
type of output is quite common for those studies that only used
coding techniques, but do not make a theoretical contribution.
A significant number of articles in our sample did not provide
sufficient details for reviewers or readers to evaluate their
methodological rigor. Several factors may contribute to a lack of
methodological detail, including space constraints and simply not
knowing what details to report. Since we only analyzed journal
articles (rather than conference papers), space constraints are less
valid as an excuse. Most seminal works on GT focus on how to
collect and analyze data rather than what details to give in the
methodology section of a paper. We therefore provide some
general advice for reporting grounded theory studies followed by
a list of specific details to include.
5.1 Method Slurring
Several SE articles claim to use grounded theory, yet do not
appear to embrace its core characteristics (Sec. 2.2). If a study
does not involve simultaneous data collection and analysis,
constant comparison, coding, memoing and theory development,
it is not a grounded theory study. If researchers collect most or all
of their data before beginning analysis, collect or categorize data
according to existing theory, base analysis on seed categories or
preconceived analytical frameworks, they are not using grounded
Claiming to use a research method without actually following its
guidelines is referred to as method slurring[3]. Based on other
authors’ and our own observations, we suggest researchers might
commit method slurring for at least five reasons:
1. To confer legitimacy. Grounded theory is more structured
(and is therefore often perceived as more scientific) than
other methods of building theories from primarily unstruc-
tured data. Charmaz lamented that “Numerous researchers
have invoked grounded theory as a methodological rationale
to justify conducting qualitative research rather than adopt-
ing its guidelines to inform their studies” [16].
2. To avoid detailed and exhaustive literature review and
initial conceptualization: Many researchers may readily
embrace the grounded theory maxim of avoiding becoming
too familiar with the relevant literature, to excuse skipping
necessary background work [72].
3. For simplicity. It is easier to state “we used grounded
theory” than to thoroughly explain how a researcher
converted a large amount of unstructured text into a cohesive
theory. However, given that grounded theory is not widely
understood (misunderstood, even) or known amongst SE
researchers, we argue that such claim does not suffice.
4. Because they simply do not understand GT or its
relationship to other research methods. Suddaby notes that
researchers claim to have performed grounded theory
research, support their claims with a cursory citation to Gla-
ser and Strauss (1967),while offering little description of
the applied method. When authors are invited to elaborate,
Suddaby continues, to reveal how the data were collected
and analyzed, it becomes clear that the term ‘grounded
theory’ was interpreted to mean ‘anything goes’ ” [72].
5. Per referee’s suggestion. We know of cases where referees
have suggested to authors that the method they used “looks
like grounded theory.” Such authors may post-hoc present
their research as grounded theory where such a claim is not
valid, simply to satisfy reviewers.
Method slurring undermines grounded theory. Authors in the
management literature have observed an “overly generic use of
the term ‘grounded theory’ [72]. Researchers in Information
Systems (which has considerable overlap with software
engineering) have lamented that the term ‘grounded theory’
itself has almost become a blanket term for a way of coding data”
[75]. Others have referred to the “erosion of GT as a research
method” [25, 64]. Using the term ‘grounded theory’ to denote any
kind of theory building or qualitative data analysis undermines the
legitimacy of GT, which prescribes a highly structured analytical
approach. This engenders undue suspicion of GT studies, possibly
hindering publication.
Similarly, it undermines other qualitative methods. Grounded
Theory is not the only valid method of either analyzing
predominately qualitative data or generating theories. Numerous
other qualitative methods exist [20]. Recasting interpretive
interview studies, positivist case studies and ethnographies as
grounded theory implicitly disparages and devalues these
legitimate research approaches. There is nothing wrong with
conducting an ethnography, for example, and researchers should
not be hesitant to label their research as such. Theories can also be
developed based on intuition and experience, or by extending and
synthesizing existing research.
Furthermore, method slurring misrepresents the current research.
A key principle of science communication is accurately describing
how data was collected and analyzed [32]. This allows reviewers
and readers to evaluate the quality of a study. If a study claims to
have used GT while actually doing something different, it violates
this principle.
Because so many GT articles lack methodological detail, it is
difficult for readers to assess whether studies actually use
grounded theory or simply reference grounded theory “as a
methodological rationale” [16].
5.2 Considerations for Conducting and
Reporting Grounded Theory
Individual researchers will have their own styles and preferences
for conducting and reporting their studies. However, to avoid
method slurring (among other problems) we offer four broad
best ‘fit’ for our data was the Six C’s coding family [30,31,42] which
describes a category in terms of its Contexts, Conditions, Causes,
Consequences, Contingencies, and Covariances.
In the following section, we describe our results — the impact of
inadequate customer involvement on self-organizing Agile teams
— in terms of the Six C’s theoretical model for the category Lack
of Customer Involvement. Using the Six C’s model, we describe (1)
Contexts: the ambiance, that is, the context of the Agile develop-
ment teams in NZ and India, (2) Conditions: factors that are prereq-
uisites for the category, Lack of Customer Involvement, to manifest,
(3) Causes: reasons that cause lack of customer involvement, (4)
Consequences: outcomes or effects of lack of customer involve-
ment on self-organizing Agile teams, (5) Contingencies: moderat-
ing factors between causes and consequences, that is, Agile
Undercover strategies, and (6) Covariances: correlations between
different categories, that is, how Agile Undercover strategies change
when factors that cause Lack of Customer Involvement change.
4. Results
In the following sections we present our theory. We have
adapted Glaser’s Six C’s model diagram [30] to illustrate our theory
of lack of customer involvement (Fig. 2). The category Lack of Cus-
tomer Involvement is at the center of the diagram. Each of the Six
C’s are represented in the other rectangles in relation to the central
category, with corresponding subsection numbers (in circles)
where we describe them.
In the following sections, we have selected quotations drawn
from our interviews that shed particular light on the concepts.
Due to space reasons we cannot describe all the underlying key
points, codes, and concepts from our interviews and observation
that further ground the discussion.
4.1. Context
We interviewed 30 Agile practitioners from 16 different soft-
ware development organizations over 3 years, half of whom where
from New Zealand and half from India. Fig. 3 shows the partici-
pants and project details. In order to respect their confidentiality,
we refer to the participants by numbers P1–P30. All the teams
were using Agile methods, primarily combinations of Scrum and
eXtreme Programming (XP) — two of the most popular Agile meth-
ods today [14,19,13]. The teams practiced Agile practices such as
iterative development, daily stand-ups, release and iteration plan-
ning, test driven-development (TDD), continuous integration and
others. Participants’ organizations offered products and services
such as web-based applications, front and back-office applications,
and local and off-shored software development services.
The level of Agile experience varied across the different teams.
While some teams had under a year of experience, others had been
practicing Agile for over 5 years. The Indian teams were mostly
catering to off-shored customers in Europe and USA and most of
the NZ teams were catering to in-house customers, some of whom
were located in separate cities. We include more details of the con-
text in sections below as necessary.
4.2. Condition
Agile projects expand the role of the customer in software
development by involving them in writing user stories, discussing
product features, prioritizing the feature lists, and providing rapid
feedback to the development team on a regular basis [11,1,9,4].
The level of customer involvement that Agile demands is higher
than their involvement in traditional projects:
‘‘Commitment for that time from business... that’s something that
isn’t normally there in a [traditional] software development project
because [customers] throw [the project] over the wall and don’t
have to worry about it for 6 months!’’ — P5, Agile Coach, NZ
Most participants did not receive the level of customer involve-
ment that Agile methods demand (P1–P12, P14–P19, P21–P23,
P25, P26, P28–P30).
‘‘Sometimes [customers] only want to come back and see in 6
months what happened [in development].’’ — P16, Developer,
‘‘To get client involved in the process I think is the most difficult
part of Agile.’’ — P4, Developer, NZ
Fig. 2. The theory of Lack of Customer Involvement depicted using the Six C’s model (Context, Condition, Causes, Consequences, Contingencies, and Covariance).
524 R. Hoda et al. / Information and Software Technology 53 (2011) 521–534
Firstly, it is important to study grounded theory before starting. As
several authors have noted, grounded theory may suffer from its
‘apparent simplicity’ [31]. Superficially, GT appears to involve
simply reading and categorizing some text. However, a key
challenge in GT is that of theoretical sensitivity: a researcher’s
capability to develop useful and interesting concepts that
contribute to a theory. Furthermore, GT is a complicated research
method with multiple variants and conflicting guidance. Many
overviews and guiding literature for SE researchers do not even
include grounded theory (cf. [27, 77]). Anyone considering a GT
study should read several books before even deciding whether GT
is the right method, let alone beginning data collection. Good
introductions are available for classic GT [34, 38], Straussian GT
[19], and constructivist GT [16]. Our review contains numerous
exemplars (e.g. [2, 18, 45, 47, 49]), which may be consulted. GT
should be considered from the conception of a study as it differs
in quite significant ways from traditional studies as outlined in
Sec. 2. Research cannot be reconstructed as GT at write-up.
Secondly, researchers should describe their implementation of
GT, not GT in principle. Some studies in our sample provided
quite reasonable summaries of GT, but did not explain their
practices, deviations or precisely what they did. Because GT is
relatively new to SE, and to avoid method slurring, it is crucial to
explain exactly what was done in the study at hand. In particular,
we recommend explicitly describing how key practices (e.g.
simultaneous data collection and analysis, constant comparison,
memoing) were used. We also recommend explicitly describing
deviations from GT guidelines.
Thirdly, researchers should avoid ‘borrowing’ rhetoric. If
techniques have been borrowed from the grounded theory
literature, researchers should simply state that those techniques
have been used without discussing GT. Practices including
coding, memoing and constant comparison are all part of the
contemporary qualitative data analyst’s toolbox. They can exist on
their own, independent of their proponents or any particular
research method. Bringing in GT clouds the issue.
Finally, and related to the previous point, researchers should not
claim to have used grounded theory when they have not.
Researchers should describe how they analyzed data or generated
theory. If using another method it should be named. If a
researcher has developed his/her own method, it should be
explained. If a researcher has proceeded ad hoc, such a
“pragmatic, agile approach” should be explained rather than
dressing it up as grounded theory. To be clear, we accept any
variant of GT as grounded theory, in contrast to Glaser who only
recognizes classicGT (as described in the ‘Discovery’ book)
and considers Straussian GT not to be GT [35] (p. 123).
We further provide an extensive list of considerations for
grounded theory in software engineering (Fig. 5), which include a
variety of potentially relevant issues for consideration when
conducting or evaluating a GT study. The items in Fig. 5 may be
especially useful for novices writing their first GT study, experts
who need to jog their memories for methodological dimensions to
address, or anyone who struggles to explain how they collected
and analyzed predominately qualitative data. The items in Fig. 5
are synthesized from existing methodological guidance for GT
and predominately qualitative studies (cf. [16, 24, 28, 78]), as well
as our own experience in conducting qualitative studies. No single
article can or should include all of these items. Instead, we offer
them as a reminder of “questions to ask oneself” before and
Figure 5. Specific considerations for conducting and reporting grounded theory
General Grounded Theory Issues
What variant of grounded theory have you adopted? What
published guidance did you follow?
How and why have you adapted, or deviated from, this variant
and guidance?
State the research area or research questioneither your initial
question, the question that emerged during your study, or
preferably both.
State your epistemological and ontological positions (e.g.
interpretivism, critical realism).
State the duration of the study.
Site Selection and Description
What organization, team, dataset, etc. did you study?
Why did you study this data?
Describe the context of the study (e.g. the kind of organization,
who is involved, what kind of software is being developed).
Role of the Literature in the Grounded Theory Study
Did you begin data collection with a clean theoretical slate?
What topic areas did you review before and during the study?
How does the literature inform, support or refute your analysis
and results?
Presenting and Evaluating Grounded Theory
Is the theoretical contribution clearly stated?
Is the generated theory integrated back into the literature?
Is the theory evaluated? If so, using which criteria?
How might your own biases, preconceptions, background and
beliefs affect your analysis?
Grounded Theory Data Collection and Analysis
What data was collected (e.g. field notes, documents,
emails, video of meetings), how and when?
Who collected and analyzed the data? Was it an individual
researcher or research team? If a team, who did what? How
was this coordinated?
Describe the pacing of analyzing data, and how it continued
throughout the project.
Describe your coding, memoing and sorting with examples.
Describe the emergence of your core category, and how this
affected your analysis.
If using classic GT, did you use any of Glaser’s coding
families? If so, which, and did the theoretical codes earn
their way into the theory?
If using Straussian GT: state how you used the conditional
How and where was your data stored? How did you manage
the volume and heterogeneity of data?
Describe your theoretical sampling with examples.
Confirm that you employed constant comparison.
When did you stop collecting data? Describe how
theoretical saturation became apparent.
Describe how the selected GT variant affected data
collection and analysis.
Did you conduct a reliability check; i.e., have your analysis
reviewed by someone else. If so, who, how, what did they
find and what changes resulted? Describe their expertise.
during a study and write-up. Simply confirming that a study
follows the various core GT guidelines (e.g. simultaneous data
collection and analysis, constant comparison, theoretical
saturation) should be unnecessary. However, because GT is still
relatively new to software engineering, and our study
demonstrates some confusion about how GT works, clearly
describing what was done and enumerating adherence to core
guidelines will benefit readers, reviewers and editors.
5.3 Challenges in Doing Grounded Theory
Research in Software Engineering
Software development contexts present several unusual challenges
for grounded theory research. Most of the GT research we have
read relies primarily on interviews and documents. However,
software contexts provide diverse data sources including: source
code, test suites, code commit logs, task and effort data from
project management software, design diagrams (e.g. wireframes,
class diagrams), design documents, domain models (e.g.
scenarios, personas, user stories, use cases), project management
documents (e.g. backlogs, burn-down charts), performance data,
issue tracker data, photos of temporary diagrams (e.g. on white
boards), online discussions (e.g. on IRC or Slack), contracts and
financial statements. Combining these with the usual data (i.e.
audio/video recordings of interviews and meetings, documents,
email, field notes) exacerbates at least three challenges:
1. Managing large amounts of heterogeneous data. Version
control, project management, team communication systems
and other technical affordances make it easy to get access to
an enormous, unreadable dataset. Capturing, storing,
indexing and managing all this data is practically
challenging. Systems appropriate for storing some data types
(e.g. NVivo for audio, video, transcripts and documents) may
be unsuitable for storing other data types (e.g. code).
Determining what to read when you have more text than you
can read in a lifetime is even more challenging. The
implications of data magnitude for theoretical sampling
remain unclear. However, one strategy is to choose an
explicit primary data source (e.g. interviews) and
theoretically sample from the remaining data based on leads
arising from the primary data source.
2. Coding unconventional texts. While they may apply more
broadly, the coding approaches associated with GT (e.g.
open and theoretical coding) were developed primarily for
analyzing unstructured text. It is not clear how to apply open
coding to design diagrams, structured text (e.g. use cases) or
source code. One approach is to open-code unstructured text
and move directly to memoing for more structured data.
Another is to adopt completely different analytical
techniques; for instance, static code analysis.
3. Cross-referencing participant statements with records.
Participants’ post-hoc reconstructions of how and why they
performed certain actions are less reliable than, for example,
their accounts of their current frustrations or enduring values.
Source code, commit logs, project management data and di-
rect observation allow the researcher to triangulate many in-
terviewee claims. This presents myriad challenges regarding
not only how to triangulate but also how to resolve conflict-
ing evidence.
Grounded theory is an increasingly popular research method in
software engineering (see Fig. 2). However, grounded theory is
complex and demanding, with several variants and conflicting
guidance, and software engineering researchers may not be
cognizant of its historical development or appreciate the
differences across its three main variants. This paper aims to draw
attention to this issue and to report on the use of Grounded Theory
in SE. The contributions of this paper are fourfold:
1. We provide a detailed comparison of the three main variants
of grounded theory, which may help aspiring grounded
theory researchers in software engineering to select the
variant that suits them best (Sec. 2);
2. Based on an analysis of almost 100 articles in nine prominent
SE journals, we found that many SE articles do not generate
a theory, do not clearly indicate which variant of grounded
theory is used and do not provide sufficient methodological
detail for rigorous evaluation (Sec. 4);
3. We offer integrated guidance for conducting and reporting
grounded theory research in software engineering, including
a set of suggestions for explaining the study’s data collection
and analysis procedures (Fig. 5);
4. We enumerate substantial challenges peculiar to conducting
GT research in software engineering, including the
proliferation of heterogeneous unstructured, semi-structured
and structured data (Sec. 5).
These contributions should be interpreted in light of several
limitations. We limited our study to those articles published in
nine well-known software engineering journals. While we believe
these journals are a reasonable surrogate for the broader SE
literature, the field has many more, including journals which are
focused on specific research areas (e.g. Requirements
Engineering). We also excluded conference papers, reasoning that
page limits would force authors to include less methodological
detail. Further sampling bias could come from articles missed due
to our specific search string and search strategy, or due to
publication bias. Furthermore, we can only analyze the way each
study is reported rather than how it was done. A few missing
methodological details clearly does not mean that the research is
poor or that the authors are unskilled. Our review simply reveals
that more methodological detail is needed and suggests potential
details to include in future articles.
We believe that grounded theory offers a highly suitable
methodology to address social, cultural and human aspects in
software engineeringseveral GT studies in SE have contributed
novel and rich insights. As described above, software engineering
presents non-trivial challenges for grounded theory research.
However, grounded theory remains one of the most rigorous
methods to generate new theories. This is a significant issue as the
establishment of a strong theory base has been identified as an
important challenge for the software engineering discipline [50,
65, 66]. We believe well conducted GT studies can make
significant contributions to our field and help to develop rich
theories to inform future empirical studies in SE.
We thank Lutz Prechelt and the anonymous reviewers for
constructive feedback. This work was supported, in part, by
Science Foundation Ireland grant 13/RC/2094 to Lerothe Irish
Software Research Centre (; the Irish Research
Council New Foundations Scheme 2014; Enterprise Ireland grant
IR/2013/0021 to ITEA2-SCALARE (; and the
Royal Irish Academy under the Charlemont Award Programme.
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... We calculated the interrater reliability agreement until we reached substantial interrater reliability> 0.60, using the Cohen Kappa's coefficient for measuring observer agreement for categorical data [44]. Throughout the study, our researchers consistently compared data, memos, codes and categories [64]. The researchers evolved the interpretation of the data and categories until theoretical saturation. ...
... The researchers evolved the interpretation of the data and categories until theoretical saturation. Theoretical saturation refers to when the researchers' interpretation is supported by the data, and new data does not prompt additional revisions or interpretations of the results [64]. We reached theoretical saturation when we noticed that our thematic coding process for new interview transcripts did not reveal any additional codes or categories or new interpretations of our results. ...
Full-text available
Building a shared understanding of non-functional requirements (NFRs) is a known but understudied challenge in requirements engineering, primarily in organizations that adopt continuous software engineering (CSE) practices. During the peak of the COVID-19 pandemic, many CSE organizations complied with working remotely due to the imposed health restrictions; many organizations continue to work remotely while implementing business processes to facilitate team communication and productivity. In remote CSE organizations, managing NFRs becomes more challenging due to the limitations of team communication. While previous research has identified the factors that lead to a lack of shared understanding of NFRs in CSE, we still have a significant gap in understanding how CSE organizations, particularly in remote work, build a shared understanding of NFRs. We conduct a 6-month ethnography-informed case study of a remote CSE organization. We identify a number of practices for building a shared understanding of NFRs, such as validating NFRs through feedback. We also studied the practices of remote collaboration and in particular, the use and affordances offered by the collaborative workspace Gather that the organization used for remote interaction; our findings suggest that it allows for informal communications instrumental for building shared understanding. In addition, we describe the limitations to building a shared understanding of NFRs in the organization, such as gaps in communication and the limited understanding of customer context. Furthermore, we conducted further interviews to validate our findings for relevance and to gain additional insights into the shared understanding of NFRs within the organization. As actionable insights, we discuss our findings in light of proactive practices that represent opportunities for software organizations to invest in building a shared understanding of NFRs in their development.
... Audio-recordings from the training programme were transcribed verbatim and researchers became familiar with the recordings before the data coding began. Line-by-line examination of the first and second learning group transcripts helped identify codes and attach them to participants' comments (Stol et al., 2016). Categories were sorted into higher-level codes, and new codes were created when necessary. ...
SEARCHING healthcare information resources can be challenging for laypeople who have limited information literacy skills. this study aims to explore strategies for enhancing the information literacy competencies of laypeople in the domains of health and information retrieval. recent health-related events such as CoVid-19, in which laypeople actively sought health-related information, reinforced the value of studies such as this one. this study uses a qualitative action learning approach to explore laypeople's experiences with information-seeking strategies. action learning groups were convened with people from patient healthcare support groups and local churches in aotearoa New Zealand, with a total of 18 participants. in-depth data was collected over one year through a total of 20 learning sessions. Grounded theory coding techniques were used for data analysis. the findings of this study demonstrate a three-step contemporary information-gathering method for laypeople, which helps with reducing the challenges of bias with commercial advertising, information overload and conflicting information that laypeople face when seeking health-related resources. the study's findings make significant contributions by using a method (action learning) that involves more engagement with participants and by proposing a model that can be useful in training laypeople in healthcare information-seeking.
... RegTech is a novelty in the market, we do not adopt a full grounded theory approach (Stol et al., 2016), but we use techniques based on grounded theory, such as theoretical sampling, memoing, memo sorting and saturation, to find and order the data. ...
Full-text available
Following the Great Financial Crisis, the emergence of digital technologies and the end of banking secrecy (“Swiss say goodbye to banking secrecy”, 2017), financial technology (FinTech) and regulatory technology (RegTech) startups have been offering products in the financial regulatory sector. This trend has increased since the outbreak of COVID-19. Most of the studies on RegTech have focused on reviewing the literature on the macro context and the issues of vast amount of regulation (Arner et al., 2017). Today the academic literature about case studies in regulatory technology is not proposing any solution of cooperation or aggregation of RegTech’s startups in Switzerland. Due to the lack of papers about RegTech in Switzerland, we adopt an approach already used for case studies in the FinTech area (Foster & Heeks, 2013; Burtch et al., 2013) through exploratory investigation through interviews and literature review. The findings of our article have allowed us to analyse the topics and the applications in the RegTech ecosystem provided by startups to Swiss banks. We examine also the aggregations, incubators, and associations active in Switzerland Swisscom, International RegTech Association (IRTA), and F10 (a Swiss FinTech incubator based in Zurich) to examine how they can bring RegTech solutions of the RegTech startups into the banks.
... The grounded theory (GT) approach involves a set of steps for data collection, analysis, formulation of theory parameters and reporting [23]. The GT helps researchers to identify common patterns across interview transcripts by constantly comparing data at different levels of abstraction [24]. This approach does not rely on a preconceived hypothesis rather aims to uncover the interviewees concerns in the process. ...
... For research question RQ2, which seeks to identify the main problems faced by practitioners involved in engineering ML-enabled systems related to problem understanding and requirements, the corresponding survey question is designed to be open text. We conducted a qualitative analysis using open and axial coding procedures from grounded theory [24] to allow the problems to emerge from the open-text responses reflecting the experience of the practitioners. The qualitative coding procedures were conducted by one PhD student and reviewed by her advisor at one site (Brazil) and reviewed independently by three researchers from two additional sites (two from Sweden and one from Turkey). ...
Conference Paper
Full-text available
Systems that use Machine Learning (ML) have become commonplace for companies that want to improve their products and processes. Literature suggests that Requirements Engineering (RE) can help address many problems when engineering ML-enabled systems. However, the state of empirical evidence on how RE is applied in practice in the context of ML-enabled systems is mainly dominated by isolated case studies with limited generalizability. We conducted an international survey to gather practitioner insights into the status quo and problems of RE in ML-enabled systems. We gathered 188 complete responses from 25 countries. We conducted quantitative statistical analyses on contemporary practices using bootstrapping with confidence intervals and qualitative analyses on the reported problems involving open and axial coding procedures. We found significant differences in RE practices within ML projects. For instance, (i) RE-related activities are mostly conducted by project leaders and data scientists, (ii) the prevalent requirements documentation format concerns interactive Notebooks, (iii) the main focus of non-functional requirements includes data quality, model reliability, and model explainability, and (iv) main challenges include managing customer expectations and aligning requirements with data. The qualitative analyses revealed that practitioners face problems related to lack of business domain understanding, unclear goals and requirements, low customer engagement, and communication issues. These results help to provide a better understanding of the adopted practices and of which problems exist in practical environments. We put forward the need to adapt further and disseminate RE-related practices for engineering ML-enabled systems.
Systems that use Machine Learning (ML) have become commonplace for companies that want to improve their products and processes. Literature suggests that Requirements Engineering (RE) can help address many problems when engineering ML-enabled systems. However, the state of empirical evidence on how RE is applied in practice in the context of ML-enabled systems is mainly dominated by isolated case studies with limited generalizability. We conducted an international survey to gather practitioner insights into the status quo and problems of RE in ML-enabled systems. We gathered 188 complete responses from 25 countries. We conducted quantitative statistical analyses on contemporary practices using bootstrapping with confidence intervals and qualitative analyses on the reported problems involving open and axial coding procedures. We found significant differences in RE practices within ML projects. For instance, (i) RE-related activities are mostly conducted by project leaders and data scientists, (ii) the prevalent requirements documentation format concerns interactive Notebooks, (iii) the main focus of non-functional requirements includes data quality, model reliability, and model explainability, and (iv) main challenges include managing customer expectations and aligning requirements with data. The qualitative analyses revealed that practitioners face problems related to lack of business domain understanding, unclear goals and requirements, low customer engagement, and communication issues. These results help to provide a better understanding of the adopted practices and of which problems exist in practical environments. We put forward the need to adapt further and disseminate RE-related practices for engineering ML-enabled systems.
Full-text available
Artificial Intelligence for IT Operations (AIOps) leverages AI approaches to handle the massive amount of data generated during the operations of software systems. Prior works have proposed various AIOps solutions to support different tasks in system operations and maintenance, such as anomaly detection. In this study, we conduct an in-depth analysis of open-source AIOps projects to understand the characteristics of AIOps in practice. We first carefully identify a set of AIOps projects from GitHub and analyze their repository metrics (e.g., the used programming languages). Then, we qualitatively examine the projects to understand their input data, analysis techniques, and goals. Finally, we assess the quality of these projects using different quality metrics, such as the number of bugs. To provide context, we also sample two sets of baseline projects from GitHub: a random sample of machine learning projects and a random sample of general-purposed projects. By comparing different metrics between our identified AIOps projects and these baselines, we derive meaningful insights. Our results reveal a recent and growing interest in AIOps solutions. However, the quality metrics indicate that AIOps projects suffer from more issues than our baseline projects. We also pinpoint the most common issues in AIOps approaches and discuss potential solutions to address these challenges. Our findings offer valuable guidance to researchers and practitioners, enabling them to comprehend the current state of AIOps practices and shed light on different ways of improving AIOps’ weaker aspects. To the best of our knowledge, this work marks the first attempt to characterize open-source AIOps projects.
The debate between ‘hard’ and ‘soft’ research approaches continues in the IS field, but with little prospect of resolution. The debate is typically characterized by tendentious arguments as advocates from each approach offer a somewhat one-sided condemnation of the counterpart from the inimical research tradition. This paper begins by relating two fictitious tales which serve to highlight the futility of research conducted at the extremity of each research approach. The dichotomies which characterize these rival factions are also summarized. The debate is then framed in terms of the polarization problem whereby IS researchers are divided geographically and paradigmatically into ‘hard’ and ‘soft’ camps. A variety of different strategies have been proposed for resolving the debate and these are discussed in detail. They are grouped into four categories, referred to as supremacism, isolationism, integration, and pluralism. Finally, the paper contends that the debate cannot be resolved, and offers the metaphor of magnetic polarity as a means of reflecting this. The paper concludes by arguing that it would be more appropriate to recast the debate at a macro level in order to accommodate different research agenda and recognize the strengths within each tradition.
- This paper describes the process of inducting theory using case studies from specifying the research questions to reaching closure. Some features of the process, such as problem definition and construct validation, are similar to hypothesis-testing research. Others, such as within-case analysis and replication logic, are unique to the inductive, case-oriented process. Overall, the process described here is highly iterative and tightly linked to data. This research approach is especially appropriate in new topic areas. The resultant theory is often novel, testable, and empirically valid. Finally, framebreaking insights, the tests of good theory (e.g., parsimony, logical coherence), and convincing grounding in the evidence are the key criteria for evaluating this type of research.
I refer to and use as scholarly inspiration CHARMAZ's excellent article on constructivist grounded theory as a tool of getting to the fundamental issues on why grounded theory is not constructivist. I show that constructivist data, if it exists at all, is a very very small part of the data that grounded theory uses.
Since the late 1960s Barney GLASER and Anselm STRAUSS, developers of the methodology of "Grounded Theory" have made several attempts to explicate, clarify and reconceptualise some of the basic tenets of their methodological approach. Diverging concepts and understandings of Grounded Theory have arisen from these attempts which have led to a split between its founders. Much of the explication and reworking of Grounded Theory surrounds the relation between data and theory and the role of previous theoretical assumptions. The book which initially established the popularity of GLASER's and STRAUSS' methodological ideas, "The Discovery of Grounded Theory", contains two conflicting understandings of the relation between data and theory - the concept of "emergence" on the one hand and the concept of "theoretical sensitivity" on the other hand. Much of the later developments of Grounded Theory can be seen as attempts to reconcile these prima facie diverging concepts. Thereby GLASER recommends to draw on a variety of "coding families" while STRAUSS proposes the use of a general theory of action to build an axis for an emerging theory. This paper first summarises the most important developments within "Grounded Theory" concerning the understanding of the relation between empirical data and theoretical statements. Thereby special emphasis will be laid on differences between GLASER's and STRAUSS' concepts and on GLASER's current critique that the concepts of "coding paradigm" and "axial coding" described by STRAUSS and Juliet CORBIN lead to the "forcing" of data. It will be argued that GLASER's critique points out some existing weaknesses of STRAUSS' concepts but vastly exaggerates the risks of the STRAUSSian approach. A main argument of this paper is that basic problems of empirically grounded theory construction can be treated much more effectively if one draws on certain results of contemporary philosophical and epistemological discussions and on widely accepted concepts developed in such debates. This especially refers to the critique of naïve empiricism, to the concept of hypothetical or abductive inference, to the concept of empirical content or falsifiability of statements and to the concept of corroboration.